Evaluation Protocol. We extract the features of our models on all images in Database, Queries, 10K distractors and 20K whitening set. Following [1]}, the features are pooled with regionalized pooling layer (R-MAC) with spatial level 3 We experimented with R-MAC and GeM both and found R-MAC to work best for SEER models which by design also L2 normalizes the features. We train the PCA whitening on 20K images and apply this whitening to the Database and Queries features. We then perform copy detection using cosine similarity between the database and query features and evaluate the performance using mean average precision (mAP) metric.
